Thursday, July 19, 2012
The ability to directly specify the closed-loop poles of a multivariable control system is a major benefit of pole-placement algorithms for calculating state-feedback and observer gains. The drawback of these algorithms is the lack of any guarantee on the stability robustness of the resulting control system. The optimal control approach for calculating state-feedback gains (LQR) has a certain guaranteed robustness, but adding an observer (i.e. Kalman filter, LQG) can result in arbitrarily poor robustness. In this talk, a new pole-placement approach is introduced for calculating state-feedback and observer gains. The new approach optimizes robustness and gives impressive results, particularly for output feedback, observer-based control systems.
University of Rhode Island